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Survey on Large Scale Neural Network Training
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training.
Efficient Combination of Rematerialization and Offloading for Training DNNs
Rematerialization and offloading are two well known strategies to save memory during the training phase of deep neural networks, allowing data scientists to consider larger models, batch sizes or higher resolution data.
Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm.
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